Chaochao Chen
ZJU Young Professor
Researches trustworthy AI, privacy-preserving machine learning and recommender systems.
Chaochao Chen's lab at Zhejiang works on trustworthy & safe ML, recommendation, search & web and interpretability, evaluation & safety of LMs. 93 papers since 2023, 62 at top venues, mostly AAAI and ICML. Applications close .
At a glance
- papers, 2023–2026
- 24/ year
- at T1 venues
- 67%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 18 papers2026 · T1 18 · collecting
- 27 papers2025 · T1 20
- 22 papers2024 · T1 18
- 26 papers2023 · T1 6
T1 venuesOtherVenues: AAAI, ICML, WWW, ACM MM, KDD
Research focuspaper titles that use each topic's words
- Recommendation, search & web49 papers
- Trustworthy & safe ML7 papers
- privacy-preserving5 papers
- Interpretability, evaluation & safety of LMs2 papers
- researches trustworthy0 papers
Paper list
From the lab homepage.
202618 papers
- ICML (T1)Demystifying the Optimal Fair Classifier in Multi-Class Classification
- AAAI (T1)FedAU^2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training
- TNNLS (T1)Incomplete Multimodal Federated Learning via Masking and Contrasting Prototypes
- ICML (T1)Leveraging Machine Unlearning for Cost-Efficient Preference Alignment
- WWW (T1)LLM-enhanced Federated Graph Learning with Geometry-aware Graph Projection and Shared Subspace Aggregation
- KDD (T1)MAPPE: Rethinking and Improving Fairness in LLMs for Medicine via Minimax Preference-based Prompt Evolution
- AAAI (T1)Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigm
- ICML (T1)Preference-Calibrated Optimization with Score-Level Distribution Alignment for Text-to-Image Diffusion Model Unlearning
- KDD (T1)ReSOT: Re-balance Semantic ID with Optimal Transport for Generative Recommendation
- KDD (T1)SCALE: Sharpness-Aware Correction for Addressing Long-Tail Effects in Combinatorial Medication Recommendation
- WWW (T1)Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation
- KDD (T1)Sharpness-aware Model Merging with salience recovery for LLM-based Cross-Domain Sequential Recommendation
202527 papers
- TKDE (T1)A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
- JMLR (T1)Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
- TKDE (T1)Cluster-Enhanced Dual Discrete Collaborative Filtering for Efficient Recommendation
- TSCContrastive Hypergraph Flows with Multifaceted Gates for Cross-Domain Sequential Recommendation
- ICLR (T1)Controllable Unlearning for Image-to-Image Generative Models via ε-Constrained Optimization
- AAAI (T1)DR-VAE: Debiased and Representation-enhanced Variational Autoencoder for Collaborative Recommendation
- TOISDuAda: Adaptive Targeted Model Poisoning Attack Framework via Dummy User Simulation on Federated Recommendation
- ICML (T1)Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization
- NeurIPS (T1)FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
- WSDM (T1)FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning
- AAAI (T1)FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation
- ICML (T1)FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models
202422 papers
- KDD (T1)CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation
- NeurIPS (T1)CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
- CIKM (T1)DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
- ICWSEnhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
- NeurIPS (T1)Federated Graph Learning for Cross-Domain Recommendation
- EMNLP (T1)Fine-grained Pluggable Gradient Ascent for Knowledge Unlearning in Language Models
- NeurIPS (T1)FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
- SIGIR (T1)Hypergraph Convolutional Network for User-Oriented Fairness in Recommender Systems
- Cell PatternsIntegration of Large Language Models and Federated Learning
- AAAI (T1)Intra- and Inter-Group Optimal Transport for User-Oriented Fairness in Recommender Systems
- AAAI (T1)Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation
- ICDE (T1)Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation
202326 papers
- IEEE UIC .An Intelligent Resource Scheduling Method With Edge Channel Deployment for BPM
- ACM MMContrastive Intra- and Inter-Modality Generation for Enhancing Incomplete Multimedia Recommendation
- TKDE (T1)Contrastive Proxy Kernel Stein Path Alignment for Cross-Domain Cold-Start Recommendation
- CHES [pdf]cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel
- ICDE [pdf]DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
- CIKM (T1)Decentralized Graph Neural Network for Privacy-Preserving Recommendation
- ACM MMDifferentially Private Sparse Mapping for Privacy-Preserving Cross Domain Recommendation
- TheWebConf : 165-176Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation
- ICDMEnhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression
- ICWSFederated Learning on Non-iid Data via Local and Global Distillation
- IJCAI (T1)Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation
- CIKM (T1)FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ZhejiangFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- Per ZJU language requirements
- Fee
- RMB 800
Not yet confirmed for this intake.
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